Lead Assistant Manager

Job Description As a Senior Data Scientist, you will play a crucial role in developing and deploying advanced machine learning models and data science solutions. You will collaborate closely with cross-functional teams to analyze data, build predictive models, and deploy solutions at scale. The ideal candidate will have a strong foundation in machine learning, statistics, and data modeling, coupled with expertise in Python and experience with distributed computing frameworks such as PySpark, Dask, and web development frameworks like FastAPI and Streamlit. Responsibilities Lead the development and implementation of machine learning models to address complex business challenges and opportunities. Conduct comprehensive data analysis to extract insights and inform decision-making processes. Collaborate with stakeholders to define project objectives, requirements, and success criteria. Develop and optimize machine learning algorithms to improve performance, efficiency, and scalability. Deploy machine learning models into production environments and ensure reliability and scalability. Monitor model performance and conduct regular evaluations to identify areas for optimization and improvement. Utilize explainable AI techniques to interpret model predictions and provide actionable insights to stakeholders. Conduct A/B testing to evaluate model performance and validate hypotheses. Leverage distributed computing frameworks such as PySpark and Dask to handle large-scale data processing tasks. Develop interactive web applications for data visualization and model deployment using FastAPI and Streamlit. Stay current with advancements in machine learning, statistics, and data science techniques and tools. Qualifications Bachelor’s or master’s degree in computer science, Statistics, Mathematics, or a related field. 4+ years of experience in data science, machine learning, or related fields. Proficiency in Python programming language for data analysis, machine learning, and web development. Strong understanding of machine learning algorithms, statistical concepts, and data modeling techniques. Hands-on experience with machine learning libraries such as NumPy, SciPy, and scikit-learn. Experience with distributed computing frameworks such as PySpark and Dask. Familiarity with model monitoring, deployment, and lifecycle management processes. Excellent problem-solving skills and attention to detail. Strong communication and collaboration skills, with the ability to work effectively in cross-functional teams. Experience with LLM models is a plus. Show more Show less